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+++ b/docs/reference/logLik.mkinfit.html
@@ -33,7 +33,21 @@
<meta property="og:title" content="Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit" />
<meta property="og:description" content="This function simply calculates the product of the likelihood densities
- calc" />
+ calculated using dnorm, i.e. assuming normal distribution.
+The total number of estimated parameters returned with the value
+ of the likelihood is calculated as the sum of fitted degradation
+ model parameters and the fitted error model parameters.
+For the case of unweighted least squares fitting, we calculate one
+ constant standard deviation from the residuals using sd
+ and add one to the number of fitted degradation model parameters.
+For the case of manual weighting, we use the weight given for each
+ observation as standard deviation in calculating its likelihood.
+In the case of iterative reweighting, the variances obtained by this
+ procedure are used in the likelihood calculations, and the number of
+ estimated parameters is obtained by the number of degradation model
+ parameters plus the number of variance model parameters, i.e. the number of
+ observed variables if the reweighting method is &quot;obs&quot;, and two if the
+ reweighting method is &quot;tc&quot;." />
<meta name="twitter:card" content="summary" />
@@ -125,7 +139,21 @@
<div class="ref-description">
<p>This function simply calculates the product of the likelihood densities
- calc</p>
+ calculated using <code>dnorm</code>, i.e. assuming normal distribution.</p>
+<p>The total number of estimated parameters returned with the value
+ of the likelihood is calculated as the sum of fitted degradation
+ model parameters and the fitted error model parameters.</p>
+<p>For the case of unweighted least squares fitting, we calculate one
+ constant standard deviation from the residuals using <code>sd</code>
+ and add one to the number of fitted degradation model parameters.</p>
+<p>For the case of manual weighting, we use the weight given for each
+ observation as standard deviation in calculating its likelihood.</p>
+<p>In the case of iterative reweighting, the variances obtained by this
+ procedure are used in the likelihood calculations, and the number of
+ estimated parameters is obtained by the number of degradation model
+ parameters plus the number of variance model parameters, i.e. the number of
+ observed variables if the reweighting method is "obs", and two if the
+ reweighting method is "tc".</p>
</div>

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